CN117521932A - Unmanned aerial vehicle inspection management system based on meshing division - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle inspection management system based on meshing division, which comprises a server, a meshing division module, an operation equipment management module, an electrical equipment management module, a user information management module, a path planning module, a task scheduling module, an operation monitoring module and a data storage module. The grid dividing module divides the unmanned aerial vehicle inspection grid by utilizing multiple factors, the path planning module constrains the inspection path according to the terrain environment factors, and the inspection path is optimized by using an ant colony algorithm combined with a 3-opt optimization algorithm. The invention solves the problems that the mesh division of the current unmanned aerial vehicle inspection management system is not comprehensive in consideration, the unmanned aerial vehicle inspection mesh is divided by utilizing multiple factors, the mesh division technology for classifying and dividing the mesh by using a genetic K-means space clustering algorithm is low in unmanned aerial vehicle inspection efficiency, and the feasible ant colony algorithm combined with the 3-opt optimization algorithm is not available for automatically generating an inspection path for path planning and comprehensively allocating unmanned aerial vehicle path planning in the mesh.
Description
Technical Field
The invention belongs to the technical field of inspection of electric unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle inspection management system based on meshing division.
Background
With the continuous deep digital transformation of the power industry, unmanned aerial vehicles are widely applied in the field of power inspection, autonomous inspection based on an unmanned aerial vehicle airport is primarily applied, but is limited by a traditional power equipment operation and maintenance management mode, and a specific feasible grid division method for dividing unmanned aerial vehicle inspection grids by utilizing multiple factors and classifying and dividing unmanned aerial vehicle inspection grids by using a genetic K-means spatial clustering algorithm is lacking. The unmanned aerial vehicle inspection mode taking a single specialty and a single team as an operation unit cannot fully mine unmanned aerial vehicle equipment resources and is difficult to exert the maximum inspection quality, so that a multi-element grid-division unmanned aerial vehicle inspection management system is needed, the unmanned aerial vehicle inspection grids are divided according to multiple factors such as wiring priority, unmanned aerial vehicle endurance, geographic environment conditions, channel hidden danger distribution conditions and the like, and an inspection path is automatically generated by carrying out path planning by combining with a 3-opt optimized ant colony algorithm, so that cross-region, cross-line and cross-specialty unmanned aerial vehicle resource sharing is promoted, and the unmanned aerial vehicle equipment utilization rate and the inspection efficiency are improved.
The Chinese patent with publication number of CN116954233A discloses an automatic matching method of a patrol task and a route, which comprises the following steps: step 1, defining task targets and constraint conditions: inputting target area map data of a patrol task, a starting point and an ending point of the task, patrol data acquisition requirements and an energy consumption model of the unmanned aerial vehicle, and outputting a route planning result; step 2, preprocessing offline map data: preprocessing the input map data; step 3, task area division: dividing the inspection area into a plurality of subareas; step 4, optimizing a particle swarm algorithm; step 5, route path smoothing processing: smoothing the route path obtained by optimization; and 6, obstacle avoidance and collision detection. The invention completes the planning and optimization of the inspection task, has certain flexibility and expansibility, and is suitable for diversified inspection application scenes. However, the unmanned aerial vehicle inspection is classified into grids based on the map data, so that the consideration factors are few, the specific situation of the inspection area is not comprehensively mastered, and the requirements of the inspection task with finer granularity cannot be met.
Disclosure of Invention
The invention provides an unmanned aerial vehicle inspection management system based on gridding division, which aims to solve the problems that the gridding division of the existing unmanned aerial vehicle inspection management system is not comprehensive enough, the gridding division technology for dividing unmanned aerial vehicle inspection grids by utilizing multiple factors and classifying and dividing the unmanned aerial vehicle inspection grids by using a genetic K-means space clustering algorithm is low in inspection efficiency in an unmanned aerial vehicle inspection mode by taking a single specialty and a single team as an operation unit, the feasible ant colony algorithm combined with 3-opt optimization is not used for automatically generating an inspection path, the unmanned aerial vehicle inspection task and planning the inspection path in the grid are allocated in a comprehensive mode, and the gridding inspection automation degree is not high.
In order to solve the technical problems, the invention provides an unmanned aerial vehicle inspection management system based on meshing division, which comprises a server, a meshing division module, a path planning module and a data storage module.
The server is used for centrally managing each module of the unmanned aerial vehicle inspection management system.
The grid division module is used for dividing unmanned aerial vehicle inspection grids according to wiring priority, unmanned aerial vehicle endurance, geographic environment conditions and channel hidden danger distribution conditions, and iterative optimization is carried out on the unmanned aerial vehicle inspection grids by using a genetic K-means spatial clustering algorithm.
The path planning module is used for constraining the inspection path according to the topographic environment factors, and planning the first inspection path by using an ant colony algorithm combining a 3-opt optimization algorithm according to the airport position, the unmanned aerial vehicle inspection task and the unmanned aerial vehicle state.
The data storage module is used for storing system data and unmanned aerial vehicle inspection data.
Preferably, the grid dividing module is used for dividing unmanned aerial vehicle inspection grids according to wiring priority, unmanned aerial vehicle endurance, geographic environment conditions and channel hidden danger distribution conditions, and the specific steps are as follows:
s1: and determining the position of the gridding control point and the standby coordinates of the unmanned aerial vehicle according to the wiring priority.
S2: and primarily dividing unmanned aerial vehicle inspection grids according to the unmanned aerial vehicle endurance and with the grid control point positions, and determining the unmanned aerial vehicle inspection grids by taking the grid control point positions as circle centers and taking 3km as radius lengths.
S3: and eliminating non-inspection areas according to the geographical environment conditions in the unmanned aerial vehicle inspection grid, expanding the unmanned aerial vehicle inspection range according to the distribution conditions of the hidden danger of the channel, and secondarily adjusting the grid boundary.
S4: and carrying out iterative optimization on the unmanned aerial vehicle inspection grid according to a genetic K-means spatial clustering algorithm.
S5: and carrying out regional division on the iterative optimized unmanned aerial vehicle inspection grid scheme, and carrying out secondary grid division according to the power transmission towers, the power transformation equipment and the power distribution towers to obtain the finally divided unmanned aerial vehicle inspection grid.
Preferably, in the step S4, the iterative optimization of the unmanned aerial vehicle inspection grid according to the genetic K-means spatial clustering algorithm specifically includes:
s41: numbering the primarily divided grids, collecting various attribute data required by grid division, including longitude and latitude coordinates of grid center points, the number of lines in the grids, the grid area and the grid topography fluctuation degree, simulating a grid-optimized mutation process by adopting a random mutation method, coding the data by utilizing a floating point coding mode, and performing normalization processing to generate an initial population.
S42: the method comprises the steps of setting constraint conditions including the number of lines in a grid, the area of the grid and the fluctuation degree of the terrain of the grid, using a K-means clustering algorithm as a fitness function, and setting parameters of a genetic algorithm including population size, selection operation, crossover probability, variation probability and iteration times, wherein the calculation formula of the fitness function specifically comprises:
wherein Maxf (x 1 ，x 2 ，x 3 ) To adapt the function, x 1 X is the number of lines in the grid 2 Is the area of the grid, x 3 Is the relief of the grid topography.
S43: calculating fitness function, evaluating individual fitness according to the fitness function, updating optimal individuals, selecting excellent individuals for selection, crossing and mutation operation, and generating a new generation population.
S44: and repeating the step S43 until the fitness function converges or the set iteration times are obtained, and outputting the optimized unmanned aerial vehicle inspection grid scheme.
Preferably, the path planning module optimizes the inspection path by adopting an improved ant colony algorithm, and specifically comprises the following steps:
a1: initializing grid path nodes, setting the number of unmanned aerial vehicles, the iteration times and the starting points of the inspection paths, initializing an pheromone matrix according to the inspection tasks, the airport positions and the unmanned aerial vehicle states, initializing the inspection paths according to the inspection tasks, and planning the initial inspection paths by taking airport deployment coordinate points as the starting points of the inspection paths.
A2: selecting a single unmanned aerial vehicle to perform optimal path search, initializing the sum of time consumption of routing inspection current path nodes and time consumption of each path to be 0, calculating a path selection probability function, selecting the path nodes to be routed through the path selection probability function by the unmanned aerial vehicle, updating the sum of time consumption of the current path nodes and time consumption of each path, resetting the unmanned aerial vehicle to a routing inspection path starting point if the selection probability of all the path nodes is 0, and traversing a path node set to be routed, wherein the calculation formula of the state transfer function is specifically as follows:
in the method, in the process of the invention,k is the kth unmanned aerial vehicle, i is the current path node of the unmanned aerial vehicle, j is the path node of the unmanned aerial vehicle selected inspection, allowed k For the set of path nodes to be patrolled, +.>Is the pheromone of the path edge from the path node i to the path node j, eta ij To the path from the path node iThe visibility alpha of the path edge of the node j is the influence factor of the pheromone on the probability of selecting the path node, beta is the influence factor of the visibility on the probability of selecting the path node, and +.>Pheromone, eta which is the path edge from the path node i to the path node delta ia (t) is the visibility of the path edge from path node i to path node delta, t c Time consumption for routing inspection of current path nodes for unmanned aerial vehicle, t ij T is the time consumption from path node i to path node j j To patrol the path node j, time consuming, t r Time consumption t for returning to unmanned aerial vehicle inspection starting point from path node j max The single longest flight duration of the unmanned aerial vehicle is achieved.
A3: if the unmanned aerial vehicle traverses all the reachable path nodes to be inspected and the path nodes to be inspected are not traversed by the unmanned aerial vehicle, randomly placing the unmanned aerial vehicle at any path node as an inspection path starting point, and repeating the step A2 until the unmanned aerial vehicle traverses the inspection path nodes to obtain a primary inspection path.
A4: and (3) repeating the steps A2 and A3 to realize that all unmanned aerial vehicles traverse the routing inspection path nodes.
A5: recording the optimal inspection path in each traversal of the step A4, performing a 3-opt optimization algorithm with the iteration number of 1000 on the optimal inspection path, and updating the pheromone matrix concentration of the optimal inspection path, wherein the 3-opt optimization algorithm specifically comprises the following steps:
a51: and selecting a connecting route between the non-adjacent 3 nodes in the path.
A52: and trying other different connection modes, calculating the path lengths after the different connection modes, and selecting the connection mode with the shortest path length as a new connection mode.
A53: the 3 edges are swapped each time to improve the initial solution until all 3 connections have no new connection patterns.
A6: repeating the steps A1-A5, performing the next iteration until the optimal solution of the shortest path is found, and outputting an optimal result.
Preferably, the calculation formula of the pheromone concentration updating function is specifically as follows:
τ ij (t+1)＝ρτ ij (t)+Δτ ij (t，t+1)
wherein τ ij (t+1) is a pheromone concentration updating function, ρ is a pheromone attenuation coefficient, and the value range is [0,1]，τ ij (t) is the pheromone contribution quantity delta tau of the unmanned plane to the path node i at the moment t ij (t, t+1) is the pheromone contribution of the unmanned aerial vehicle from the time t to the time t+1 to the path node j from the path node i.
The unmanned plane contributes to the pheromone contribution quantity delta tau from the path node i to the path node j from the time t to the time t+1 ij The calculation formula of (t, t+1) is specifically:
wherein Deltaτ k ij And (t, t+1) is the contribution quantity of the kth unmanned aerial vehicle from the time t to the time t+1 to the pheromones from the path node i to the path node j, and m is the number of unmanned aerial vehicle traversals, namely the total number of unmanned aerial vehicles.
The kth unmanned plane t time to t+1 time contributes to the pheromone contribution quantity delta tau from the path node i to the path node j k ij The calculation formula of (t, t+1) is specifically:
wherein t is k And (5) traversing the time-consuming sum of the inspection path for the kth unmanned aerial vehicle.
Preferably, the unmanned aerial vehicle inspection management system further comprises a working equipment management module, an electrical equipment management module, a user information management module, a task scheduling module and a working monitoring module.
The operation equipment management module is used for managing unmanned aerial vehicle information and airport information in each grid, and specifically comprises the following steps: unmanned aerial vehicle and airport model and parameter, airport deployment position coordinate information and unmanned aerial vehicle preparation drop point coordinate information corresponding to airport deployment position coordinates.
The electrical equipment module is used for managing electrical equipment information in each grid, and the electrical equipment information specifically comprises: the name, model, coordinates, elevation, orientation of the electrical equipment and the 3D model in the corresponding database;
the user information management module is used for setting user rights and managing user information, wherein the user rights comprise administrator user rights and common user rights: the administrator user permission comprises the added, deleted and modified checking permission of all user information, operation equipment, electrical equipment, unmanned aerial vehicle flight airspace, routing inspection paths and grid information; the common user permission comprises viewing permission of all operation equipment, electrical equipment, unmanned aerial vehicle flight airspace, routing inspection paths and grid information.
The task scheduling module is used for distributing, modifying or terminating the unmanned aerial vehicle inspection task to the airport according to the task priority, airport busyness and attribution authority optimization.
The operation monitoring module is used for monitoring states of the unmanned aerial vehicle, the airport and the task in real time.
Preferably, the task scheduling module allocates, modifies or terminates the unmanned aerial vehicle inspection task to the airport according to the task priority, airport busyness and attribution authority optimization, and specifically comprises the following steps:
the task priority is the input task priority, and the task scheduling module judges whether to execute the task immediately or in a delayed manner according to the task priority.
The airport busyness is the number of all task lists in the current airport, the time required for completing all tasks is quantized, the airport busyness of all airports is considered when a task scheduling module allocates a certain task, and the task is only allocated in the airports meeting the conditions.
The attribution authority is optimal, namely, the task scheduling module can only schedule the users inputting the unmanned aerial vehicle inspection tasks to belong to the airport governed by the unit, and the nearest airport is not directly scheduled.
Preferably, the operation monitoring module is used for monitoring the states of the unmanned plane, the airport and the task in real time, specifically:
acquiring unmanned aerial vehicle and airport data, wherein the airport data comprise airport states of the unmanned aerial vehicle, surrounding environments of the airport and parameters of the unmanned aerial vehicle, and if an operation monitoring module monitors that the airport states are abnormal, the surrounding wind speed of the airport is greater than the maximum wind resistance speed of the unmanned aerial vehicle or the state of the unmanned aerial vehicle is abnormal, and normal inspection operation of the unmanned aerial vehicle is influenced, the operation monitoring module sends the airport or the unmanned aerial vehicle to a task scheduling module so that the unmanned aerial vehicle inspection task cannot be executed; if the operation monitoring module monitors that the state of the unmanned aerial vehicle is abnormal in the process of executing the task, an instruction for immediately executing hovering or returning is sent to the unmanned aerial vehicle.
The task execution state data comprises a task number, a task time length, a double number of the inspection equipment and inspected data, and the task execution state data assist a user in knowing task information and managing historical task execution conditions.
Compared with the prior art, the invention has the following technical effects:
1. the inspection management system based on gridding division is beneficial to grasping the conditions of terrain shielding, signal distribution, equipment distribution and the like in each grid, comprehensively allocating unmanned aerial vehicle inspection tasks in the grid, planning inspection routes, improving gridding inspection stability and expanding gridding inspection radius; the method is favorable for making targeted operation and maintenance measures, increases the operation and maintenance working force of the line and ensures the safe operation of the important transmission line. Meanwhile, aiming at the period that the requirements on timeliness are high in emergency or other special patrol conditions such as typhoons, floods and mountain fires, the system can start an emergency mode, generate an emergency route in time and flexibly allocate unmanned aerial vehicle resources.
2. According to the invention, the genetic K-means spatial clustering algorithm is used for carrying out iterative optimization on the unmanned aerial vehicle inspection grid, the genetic algorithm is improved by combining with the K-means spatial clustering algorithm, and the position of the clustering center is automatically adjusted according to the characteristics of data, so that the genetic algorithm is prevented from being trapped into a local optimal solution in the searching process, and the grid dividing precision and accuracy are improved.
3. The invention uses the improved ant colony algorithm to carry out path planning and generate the inspection path, and the 3-opt algorithm has higher efficiency and expandability and can be applied to the path problems of different scales and complexity. The advantages of the respective algorithms can be fully developed by combining the ant colony algorithm and the 3-opt algorithm, the global searching capability and the local optimizing capability of the algorithms are improved, the routing inspection path is further improved on the basis of the initial solution found by the ant colony algorithm, the routing inspection path is enabled to be closer to the optimal solution, and the searching efficiency of the ant colony algorithm and the accuracy of finding the shortest path are improved.
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Fig. 1 is a diagram of an overall structure of an unmanned aerial vehicle inspection management system based on meshing division;
fig. 2 is a grid partitioning flow chart of the grid partitioning module according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present application and with reference to the accompanying drawings.
Example 1
The invention provides an unmanned aerial vehicle inspection management system based on meshing division, which comprises a server, a meshing division module, a path planning module and a data storage module.
The server is used for centrally managing each module of the unmanned aerial vehicle inspection management system.
The grid dividing module is used for dividing unmanned aerial vehicle patrol grids according to the distribution condition of power grid equipment, the signal intensity condition, the unmanned aerial vehicle endurance capacity, the geographic environment condition and the channel hidden danger distribution condition.
The path planning module is used for restricting the routing inspection path according to the terrain environment factors, namely, the micro-terrain conditions which need to meet the geographic environment including barriers such as forests, buildings and the like inspected by unmanned aerial vehicles and have flight inspection risks can not have cross parts with the air route. And optimizing the inspection path by using an improved ant colony algorithm according to the airport position, the unmanned aerial vehicle inspection task and the unmanned aerial vehicle state, namely the unmanned aerial vehicle maximum endurance.
The data storage module is used for storing system data and unmanned aerial vehicle inspection data, the data storage module stores inspection photos or videos according to different inspection paths, and performs standard naming on the photos of the same category, and the standard naming format of the photos can be as follows:
voltage class + line name + pole tower number + photographed device location.
The video specification naming format may be as follows:
voltage class + line name + pole tower number + visible light (infrared) +shooting date + video number.
Further, the grid dividing module is used for dividing unmanned aerial vehicle inspection grids according to wiring priority, unmanned aerial vehicle endurance, geographic environment conditions and channel hidden danger distribution conditions, and the specific steps are as follows:
s1: the position of the gridding control point and the coordinates of the unmanned aerial vehicle are determined according to the wiring priority, namely, the gridding control point and the coordinates of the standby point are determined according to the distribution of power grid equipment and the condition of signal intensity, and in a specific embodiment, the wiring priority can sequentially consider the reliability of power supply, the difficulty of standby, the difficulty of airspace application, the difficulty of installation and the difficulty of maintenance, and the standby point selects an open field near the gridding control point.
S2: and primarily dividing unmanned aerial vehicle inspection grids according to the unmanned aerial vehicle endurance and with the grid control point positions, and determining the unmanned aerial vehicle inspection grids by taking the grid control point positions as circle centers and taking 3km as radius lengths.
S3: and eliminating non-inspection areas according to the geographical environment conditions in the unmanned aerial vehicle inspection grid, expanding the unmanned aerial vehicle inspection range according to the distribution conditions of the hidden danger of the channel, and secondarily adjusting the grid boundary. The geographical environment comprises barriers such as forests and buildings which influence unmanned aerial vehicle inspection and micro-terrain conditions with flight inspection risks, and the secondary adjustment of the grid boundary according to the distribution condition of the channel hidden danger is to obtain the distribution of the channel hidden danger in the grid and expand the grid boundary according to a channel hidden danger identification algorithm, and the specific steps of the channel hidden danger identification algorithm are as follows:
s31: and acquiring an aerial image of the unmanned aerial vehicle.
S32: and (3) carrying out hidden danger identification by adopting a trained semantic segmentation model, wherein the model can identify hidden danger of a greenhouse, a mulching film, a garbage pile and a moso bamboo channel, and outputting a mask graph after identification.
S33: the outline of the recognition object in the mask graph is extracted, the outline is converted into polygonal pixel coordinates through an outline fitting method, and in the embodiment, the outline fitting adopts a cv2.approxpolydp method of openCV.
S34: and converting each object into projection coordinates according to the pixel coordinates, generating a polygon object, and generating shp files by all the objects.
The formula for converting the pixel coordinates into projection coordinates is as follows:
wherein (X) 0 ，Y 0 ) Is the image start coordinate, (X) 1 ，Y 1 ) Is pixel coordinates, (X) 2 ，Y 2 ) For projection coordinates, S is the pixel size. Wherein (X) 0 ，Y 0 ) S is known information in mask diagram, (X) 1 ，Y 1 ) And extracting the contour coordinates of the mask map after contour fitting.
S4: and carrying out iterative optimization on the unmanned aerial vehicle inspection grid according to a genetic K-means spatial clustering algorithm.
S5: and carrying out regional division on the iterative optimized unmanned aerial vehicle inspection grid scheme, and carrying out secondary grid division according to the power transmission towers, the power transformation equipment and the power distribution towers to obtain the finally divided unmanned aerial vehicle inspection grid.
Further, in the step S4, the iterative optimization of the unmanned aerial vehicle inspection grid according to the genetic K-means spatial clustering algorithm specifically includes:
s41: numbering the primarily divided grids, collecting various attribute data required by grid division, including longitude and latitude coordinates of grid center points, the number of lines in the grids, the grid area and the grid topography fluctuation degree, simulating a grid-optimized mutation process by adopting a random mutation method, coding the data by utilizing a floating point coding mode, and performing normalization processing to generate an initial population.
S42: setting constraint barThe element comprises the number of lines in a grid, the area of the grid and the relief degree of the topography of the grid: the number of lines in the grid is not less than 50, and the area of the grid is not less than 30km 2 The relief (elevation drop) is not more than 500m. Setting a fitness function according to constraint conditions, and using a K-means clustering algorithm as the fitness function, wherein the fitness function is specifically as follows:
wherein Maxf (x 1 ，x 2 ，x 3 ) To adapt the function, x 1 X is the number of lines in the grid 2 Is the area of the grid, x 3 Is the relief of the grid topography.
Parameters of the algorithm include population size, selection operation, crossover probability, variation probability and iteration number: there is no upper limit on the number of meshing but the above constraints are met. The population size is set to 50, the selection operation is random selection, the crossover probability is set to 0.95, the variation probability is set to 0.01, and the iteration number is set to 200.
S43: calculating fitness function, evaluating individual fitness according to the fitness function, updating optimal individuals, selecting excellent individuals for selection, crossing and mutation operation, and generating a new generation population.
S44: and repeating the step S43 until the fitness function converges or the set iteration times are obtained, and outputting the optimized unmanned aerial vehicle inspection grid scheme.
Further, the path planning module adopts an improved ant colony algorithm to optimize the inspection path, and specifically comprises the following steps:
a1: initializing grid path nodes, and setting the number of unmanned aerial vehicles, the iteration times and the starting points of routing inspection paths: the number of unmanned aerial vehicles is set to 3, the iteration times are set to 100, an pheromone matrix is initialized according to the inspection task, the airport position and the unmanned aerial vehicle state, an inspection path is initialized according to the inspection task, and an initial inspection path is planned by taking airport deployment coordinate points as the take-off and landing points of the inspection path.
A2: selecting a single unmanned aerial vehicle to perform optimal path search, initializing the sum of time consumption of routing inspection current path nodes and time consumption of each path to be 0, calculating a path selection probability function, selecting the path nodes to be routed through the path selection probability function by the unmanned aerial vehicle, updating the sum of time consumption of the current path nodes and time consumption of each path, resetting the unmanned aerial vehicle to a routing inspection path starting point if the selection probability of all the path nodes is 0, and traversing a path node set to be routed, wherein the calculation formula of the state transfer function is specifically as follows:
in the method, in the process of the invention,k is the kth unmanned aerial vehicle, i is the current path node of the unmanned aerial vehicle, j is the path node selected for inspection by the unmanned aerial vehicle, allowedk is the path node set to be inspected, and +.>Is the pheromone of the path edge from the path node i to the path node j, eta ij The visibility alpha of the path edge from the path node i to the path node j is the influence factor of the pheromone on the probability of the path node, the value is 1, the value is 2, and the value is beta>Pheromone, eta which is the path edge from the path node i to the path node delta iδ (t) is the visibility of the path edge from path node i to path node delta, t c Time consumption for routing inspection of current path nodes for unmanned aerial vehicle, t ij T is the time consumption from path node i to path node j j To patrol the path node j, time consuming, t r Time consumption t for returning to unmanned aerial vehicle inspection starting point from path node j max The single longest flight duration of the unmanned aerial vehicle is achieved.
A3: if the unmanned aerial vehicle traverses all the reachable path nodes to be inspected and the path nodes to be inspected are not traversed by the unmanned aerial vehicle, randomly placing the unmanned aerial vehicle at any path node as an inspection path starting point, and repeating the step A2 until the unmanned aerial vehicle traverses the inspection path nodes to obtain a primary inspection path.
A4: and (3) repeating the steps A2 and A3 to realize that all unmanned aerial vehicles traverse the routing inspection path nodes.
A5: recording the optimal inspection path in each traversal of the step A4, performing a 3-opt optimization algorithm with the iteration number of 1000 on the optimal inspection path, and updating the concentration of the pheromone matrix on the optimal inspection path. The 3-opt optimization algorithm specifically comprises the following steps:
a51: and selecting a connecting route between the non-adjacent 3 nodes in the path.
A52: and trying other different connection modes, calculating the path lengths after the different connection modes, and selecting the connection mode with the shortest path length as a new connection mode.
A53: the 3 edges are swapped each time to improve the initial solution until all 3 connections have no new connection patterns.
A6: repeating the steps A1-A5, performing the next iteration until the optimal solution of the shortest path is found, and outputting an optimal result.
Preferably, the calculation formula of the pheromone concentration updating function is specifically as follows:
τ ij (t+1)＝ρτ ij (t)+Δτ ij (t，t+1)
wherein τ ij (t+1) is a pheromone concentration updating function, ρ is a pheromone attenuation coefficient, and the value range is [0,1]，τ ij (t) is the pheromone contribution quantity delta tau of the unmanned plane to the path node i at the moment t ij (t, t+1) is the pheromone contribution of the unmanned aerial vehicle from the time t to the time t+1 to the path node j from the path node i.
The unmanned plane contributes to the pheromone contribution quantity delta tau from the path node i to the path node j from the time t to the time t+1 ij The calculation formula of (t, t+1) is specifically:
wherein Deltaτ k ij And (t, t+1) is the contribution quantity of the kth unmanned aerial vehicle from the time t to the time t+1 to the pheromones from the path node i to the path node j, and m is the number of unmanned aerial vehicle traversals, namely the total number of unmanned aerial vehicles.
The kth unmanned plane t time to t+1 time contributes to the pheromone contribution quantity delta tau from the path node i to the path node j k ij The calculation formula of (t, t+1) is specifically:
wherein t is k And (5) traversing the time-consuming sum of the inspection path for the kth unmanned aerial vehicle.
Referring to fig. 1, further, the unmanned aerial vehicle inspection management system further includes an operation device management module, an electrical device management module, a user information management module, a task scheduling module and an operation monitoring module.
The operation equipment management module is used for managing unmanned aerial vehicle information and airport information in each grid, and specifically comprises the following steps: unmanned aerial vehicle and airport model and parameter, airport deployment position coordinate information and unmanned aerial vehicle preparation drop point coordinate information corresponding to airport deployment position coordinates, corresponding to fixed positions of the airport, unmanned aerial vehicle and related position information in the same grid.
The electrical equipment module is used for managing electrical equipment information in each grid, and the electrical equipment information specifically comprises: and 3D models in the name, model, coordinates, elevation and orientation of the electrical equipment and corresponding databases, wherein the 3D models carry out equipment three-dimensional modeling through an unmanned plane-mounted L1 laser radar, and the acquired PNTS point cloud models are imported into the system.
The user information management module is used for setting user rights and managing user information, wherein the user rights comprise administrator user rights and common user rights: the administrator user permission comprises the added, deleted and modified checking permission of all user information, operation equipment, electrical equipment, unmanned aerial vehicle flight airspace, routing inspection paths and grid information, and specifically comprises the following steps: managing the user information comprises a user name, a portal ID, departments, mobile phone numbers and user types, managing the operation equipment information comprises unmanned aerial vehicle and airport information in each grid, managing the electrical equipment information comprises an electrical equipment name, a model, coordinates, an elevation, an orientation and a 3D model in a corresponding database, and editing the inspection path information comprises the steps of inputting a new inspection path and deleting the input inspection path. The common user permission comprises viewing permission of all operation equipment, electrical equipment, unmanned aerial vehicle flight airspace, routing inspection paths and grid information.
The task scheduling module is used for distributing, modifying or terminating the unmanned aerial vehicle inspection task to the airport according to the task priority, airport busyness and attribution authority optimization.
The operation monitoring module is used for monitoring states of the unmanned aerial vehicle, the airport and the task in real time.
Further, the task scheduling module distributes, modifies or terminates the unmanned aerial vehicle inspection task to the airport according to the task priority, airport busyness and attribution authority optimization, and specifically comprises the following steps:
the task priority is the input task priority, and the task scheduling module judges whether to execute the task immediately or in a delayed manner according to the task priority.
The airport busyness is the number of all task lists in the current airport, the time required for completing all tasks is quantized, the airport busyness of all airports is considered when a task scheduling module allocates a certain task, and the task is only allocated in the airports meeting the conditions.
The attribution authority is optimal, namely, the task scheduling module can only schedule the users inputting the unmanned aerial vehicle inspection tasks to belong to the airport governed by the unit, and the nearest airport is not directly scheduled.
Further, the operation monitoring module is used for monitoring the states of the unmanned aerial vehicle, the airport and the task in real time, and specifically comprises the following steps:
acquiring unmanned aerial vehicle and airport data, wherein the airport data comprise airport states of the unmanned aerial vehicle, surrounding environments of the airport and parameters of the unmanned aerial vehicle, and if an operation monitoring module monitors that the airport states are abnormal, the surrounding wind speed of the airport is greater than the maximum wind resistance speed of the unmanned aerial vehicle or the state of the unmanned aerial vehicle is abnormal, and normal inspection operation of the unmanned aerial vehicle is influenced, the operation monitoring module sends the airport or the unmanned aerial vehicle to a task scheduling module so that the unmanned aerial vehicle inspection task cannot be executed; if the operation monitoring module monitors that the state of the unmanned aerial vehicle is abnormal in the process of executing the task, an instruction for immediately executing hovering or returning is sent to the unmanned aerial vehicle.
The task execution state data comprises a task number, a task time length, a double number of the inspection equipment and inspected data, and the task execution state data assist a user in knowing task information and managing historical task execution conditions. And the inspected data are automatically stored in a data storage module, and defects are manually screened or identified by artificial intelligence in the next step.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and improvements could be made by those skilled in the art without departing from the inventive concept, which falls within the scope of the present invention.
Claims (8)
1. The unmanned aerial vehicle inspection management system based on gridding division is characterized by comprising a server, a gridding division module, a path planning module and a data storage module;
the server is used for centrally managing each module of the unmanned aerial vehicle inspection management system;
the grid dividing module is used for dividing unmanned aerial vehicle inspection grids according to wiring priority, unmanned aerial vehicle endurance, geographic environment conditions and channel hidden danger distribution conditions, and carrying out iterative optimization on the unmanned aerial vehicle inspection grids by using a genetic K-means spatial clustering algorithm;
the path planning module is used for constraining a patrol path according to the topographic environment factors, and planning a first patrol path according to the airport position, the unmanned aerial vehicle patrol task and the unmanned aerial vehicle state by using an ant colony algorithm combined with a 3-opt optimization algorithm;
the data storage module is used for storing system data and unmanned aerial vehicle inspection data.
2. The unmanned aerial vehicle inspection management system based on gridding division according to claim 1, wherein the gridding division module is used for dividing unmanned aerial vehicle inspection grids according to wiring priority, unmanned aerial vehicle endurance, geographic environment conditions and channel hidden danger distribution conditions, and specifically comprises the following steps:
s1: determining the position of a gridding control point and the standby coordinates of the unmanned aerial vehicle according to the wiring priority;
s2: preliminarily dividing unmanned aerial vehicle inspection grids according to unmanned aerial vehicle endurance with the grid control point positions, and determining the unmanned aerial vehicle inspection grids with the grid control point positions as circle centers and 3km as radius lengths;
s3: removing non-inspection areas according to the geographical environment conditions in the unmanned aerial vehicle inspection grid, expanding the unmanned aerial vehicle inspection range according to the distribution conditions of the hidden danger of the channel, and secondarily adjusting the grid boundary;
s4: carrying out iterative optimization on the unmanned aerial vehicle inspection grid according to a genetic K-means spatial clustering algorithm;
s5: and carrying out regional division on the iterative optimized unmanned aerial vehicle inspection grid scheme, and carrying out secondary grid division according to the power transmission towers, the power transformation equipment and the power distribution towers to obtain the finally divided unmanned aerial vehicle inspection grid.
3. The unmanned aerial vehicle inspection management system based on meshing division according to claim 2, wherein in the step S4, iterative optimization is specifically performed on the unmanned aerial vehicle inspection grid according to a genetic K-means spatial clustering algorithm:
s41: numbering primarily divided grids, collecting various attribute data required by grid division, including longitude and latitude coordinates of grid center points, the number of lines in the grids, the grid area and the grid topography fluctuation degree, simulating a grid-optimized mutation process by adopting a random mutation method, coding the data by utilizing a floating point coding mode, and performing normalization processing to generate an initial population;
s42: the method comprises the steps of setting constraint conditions including the number of lines in a grid, the area of the grid and the fluctuation degree of the terrain of the grid, using a K-means clustering algorithm as a fitness function, and setting parameters of a genetic algorithm including population size, selection operation, crossover probability, variation probability and iteration times, wherein the calculation formula of the fitness function specifically comprises:
wherein Maxf (x 1 ，x 2 ，x 3 ) To adapt the function, x 1 X is the number of lines in the grid 2 Is the area of the grid, x 3 Is the relief of the grid terrain;
s43: calculating fitness function, evaluating individual fitness according to the fitness function, updating optimal individuals, selecting excellent individuals for selection, crossing and mutation operation, and generating a new generation population;
s44: and repeating the step S43 until the fitness function converges or the set iteration times are obtained, and outputting the optimized unmanned aerial vehicle inspection grid scheme.
4. The unmanned aerial vehicle inspection management system based on meshing division according to claim 1, wherein the path planning module optimizes the inspection path by adopting an improved ant colony algorithm, specifically:
a1: initializing grid path nodes, setting the number of unmanned aerial vehicles, the iteration times and the starting points of the inspection paths, initializing an pheromone matrix according to the inspection tasks, the airport positions and the unmanned aerial vehicle states, initializing the inspection paths according to the inspection tasks, and planning an initial inspection path by taking airport deployment coordinate points as the starting points of the inspection paths;
a2: selecting a single unmanned aerial vehicle to perform optimal path search, initializing the sum of time consumption of routing inspection current path nodes and time consumption of each path to be 0, calculating a path selection probability function, selecting the path nodes to be routed through the path selection probability function by the unmanned aerial vehicle, updating the sum of time consumption of the current path nodes and time consumption of each path, resetting the unmanned aerial vehicle to a routing inspection path starting point if the selection probability of all the path nodes is 0, and traversing a path node set to be routed, wherein the calculation formula of the state transfer function is specifically as follows:
in the method, in the process of the invention,k is the kth unmanned aerial vehicle, i is the current path node of the unmanned aerial vehicle, j is the path node of the unmanned aerial vehicle selected inspection, allowed k For the set of path nodes to be patrolled, +.>Is the pheromone of the path edge from the path node i to the path node j, eta ij For the visibility of the path edge from path node i to path node j, α is the influence factor of the pheromone on the probability of the path node selection, β is the influence factor of the visibility on the path node selection probability, and +.>Pheromone, eta which is the path edge from the path node i to the path node delta iδ (t) is the visibility of the path edge from path node i to path node delta, t c Time consumption for routing inspection of current path nodes for unmanned aerial vehicle, t ij T is the time consumption from path node i to path node j j To patrol the path node j, time consuming, t r Time consumption t for returning to unmanned aerial vehicle inspection starting point from path node j max The single longest flight duration of the unmanned aerial vehicle is set;
a3: if the unmanned aerial vehicle traverses all the reachable path nodes to be inspected and the path nodes to be inspected are not traversed by the unmanned aerial vehicle, randomly placing the unmanned aerial vehicle at any path node as an inspection path starting point, and repeating the step A2 until the unmanned aerial vehicle traverses the inspection path nodes to obtain a primary inspection path;
a4: repeating the steps A2 and A3 to realize that all unmanned aerial vehicles traverse the routing inspection path nodes;
a5: recording the optimal inspection path in each traversal of the step A4, performing a 3-opt optimization algorithm with the iteration number of 1000 on the optimal inspection path, and updating the pheromone matrix concentration of the optimal inspection path, wherein the 3-opt optimization algorithm specifically comprises the following steps:
a51: selecting a connecting route between non-adjacent 3 nodes in the path;
a52: other different connection modes are tried, path lengths after the different connection modes are calculated, and the connection mode with the shortest path length is selected as a new connection mode;
a53: exchanging 3 edges each time to improve the initial solution until all 3 connections have no new connection means;
a6: repeating the steps A1-A5, performing the next iteration until the optimal solution of the shortest path is found, and outputting an optimal result.
5. The unmanned aerial vehicle inspection management system based on meshing division according to claim 4, wherein the calculation formula of the pheromone concentration updating function is specifically as follows:
τ ij (t+1)＝ρτ ij (t)+Δτ ij (t，t+1)
wherein τ ij (t+1) is a pheromone concentration updating function, ρ is a pheromone attenuation coefficient, and the value range is [0,1]，τ ij (t) is the pheromone contribution quantity delta tau of the unmanned plane to the path node i at the moment t ij (t, t+1) is the pheromone contribution of the unmanned aerial vehicle from the time t to the time t+1 to the path node j from the path node i;
the unmanned plane contributes to the pheromone contribution quantity delta tau from the path node i to the path node j from the time t to the time t+1 ij The calculation formula of (t, t+1) is specifically:
wherein Deltaτ k ij (t, t+1) is information of the kth unmanned plane from t time to t+1 time to the path node i to the path node jThe element contribution quantity, m is the number of times of unmanned aerial vehicle traversal, namely the total number of unmanned aerial vehicles;
the kth unmanned plane t time to t+1 time contributes to the pheromone contribution quantity delta tau from the path node i to the path node j k ij The calculation formula of (t, t+1) is specifically:
wherein t is k And (5) traversing the time-consuming sum of the inspection path for the jth unmanned aerial vehicle.
6. The unmanned aerial vehicle inspection management system based on meshing division according to claim 1, wherein the unmanned aerial vehicle inspection management system further comprises an operation equipment management module, an electrical equipment management module, a user information management module, a task scheduling module and an operation monitoring module;
the operation equipment management module is used for managing unmanned aerial vehicle information and airport information in each grid, and specifically comprises the following steps: unmanned aerial vehicle and airport model and parameter, airport deployment position coordinate information and unmanned aerial vehicle preparation drop point coordinate information corresponding to airport deployment position coordinates;
the electrical equipment module is used for managing electrical equipment information in each grid, and the electrical equipment information specifically comprises: the name, model, coordinates, elevation, orientation of the electrical equipment and the 3D model in the corresponding database;
the user information management module is used for setting user rights and managing user information, wherein the user rights comprise administrator user rights and common user rights: the administrator user permission comprises the added, deleted and modified checking permission of all user information, operation equipment, electrical equipment, unmanned aerial vehicle flight airspace, routing inspection paths and grid information; the common user permission comprises viewing permission of all operation equipment, electrical equipment, unmanned aerial vehicle flight airspace, routing inspection paths and grid information;
the task scheduling module is used for distributing, modifying or terminating the unmanned aerial vehicle inspection task to the airport according to the task priority, airport busyness and attribution authority optimization;
the operation monitoring module is used for monitoring states of the unmanned aerial vehicle, the airport and the task in real time.
7. The unmanned aerial vehicle inspection management system based on meshing division according to claim 6, wherein the task scheduling module distributes, modifies or terminates unmanned aerial vehicle inspection tasks to an airport according to task priority, airport busyness and attribution authority optimization, and specifically comprises:
the task priority is the input task priority, and the task scheduling module judges whether to execute the task immediately or in a delayed manner according to the task priority;
the airport busyness is the number of all task lists in the current airport, the time required for completing all tasks is quantized, the airport busyness of all airports is considered when a task scheduling module allocates a certain task, and the task is only allocated in the airports meeting the conditions;
the attribution authority is optimal, namely, the task scheduling module can only schedule the users inputting the unmanned aerial vehicle inspection tasks to belong to the airport governed by the unit, and the nearest airport is not directly scheduled.
8. The unmanned aerial vehicle inspection management system based on meshing division according to claim 6, wherein the job monitoring module is used for monitoring unmanned aerial vehicle, airport and task states in real time, specifically:
acquiring unmanned aerial vehicle and airport data, wherein the airport data comprise airport states of the unmanned aerial vehicle, surrounding environments of the airport and parameters of the unmanned aerial vehicle, and if an operation monitoring module monitors that the airport states are abnormal, the surrounding wind speed of the airport is greater than the maximum wind resistance speed of the unmanned aerial vehicle or the state of the unmanned aerial vehicle is abnormal, and normal inspection operation of the unmanned aerial vehicle is influenced, the operation monitoring module sends the airport or the unmanned aerial vehicle to a task scheduling module so that the unmanned aerial vehicle inspection task cannot be executed; if the operation monitoring module monitors that the state of the unmanned aerial vehicle is abnormal in the process of executing the task, an instruction for immediately executing hovering or returning is sent to the unmanned aerial vehicle;
the task execution state data comprises a task number, a task time length, a double number of the inspection equipment and inspected data, and the task execution state data assist a user in knowing task information and managing historical task execution conditions.
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